Complexity and technological evolution: What everybody knows? · 2018. 3. 7. · Complexity and...
Transcript of Complexity and technological evolution: What everybody knows? · 2018. 3. 7. · Complexity and...
Complexity and technological evolution: Whateverybody knows?
Krist Vaesen1,2 • Wybo Houkes1
Received: 28 November 2016 / Accepted: 28 October 2017 / Published online: 22 November 2017
� The Author(s) 2017. This article is an open access publication
Abstract The consensus among cultural evolutionists seems to be that human
cultural evolution is cumulative, which is commonly understood in the specific
sense that cultural traits, especially technological traits, increase in complexity over
generations. Here we argue that there is insufficient credible evidence in favor of or
against this technological complexity thesis. For one thing, the few datasets that are
available hardly constitute a representative sample. For another, they substantiate
very specific, and usually different versions of the complexity thesis or, even worse,
do not point to complexity increases. We highlight the problems our findings raise
for current work in cultural-evolutionary theory, and present various suggestions for
future research.
Keywords Cultural evolution � Cultural-evolutionary theory � Cumulative
culture � Complexity � Technology
Introduction
One aspect of contemporary culture is the rapid advance and diffusion of
technologies, many of which—surgical robots, fighter planes, the latest version of
your operating system—require highly specialized skills and knowledge to
& Krist Vaesen
http://home.ieis.tue.nl/kvaesen/
Wybo Houkes
1 Department Philosophy and Ethics, Eindhoven University of Technology, Postbox 513,
5600 MB Eindhoven, The Netherlands
2 Faculty of Archaeology, Leiden University, Einsteinweg 2, 2333 CC Leiden, The Netherlands
123
Biol Philos (2017) 32:1245–1268
https://doi.org/10.1007/s10539-017-9603-1
manufacture, maintain and operate, and have more parts than there are ants in the
average anthill. As even a cursory inspection of the history of technology shows,
this is hardly distinctive of our times: throughout the ages, humans have used tools
that grow ever more advanced, modifying inventions of earlier generations or other
communities of tool makers. Yet there seems to be something uniquely human, if
not uniquely twenty-first century human, about this: no other animal species appears
to have technologies or processes of innovation and diffusion that match ours.
It is difficult to deny the intuitive appeal of the bold claims made in the previous
paragraph, although it might be easy to think of complications and exceptions. All of
the claims also feature prominently in past and ongoing research programs on human
evolution. Here, we are concerned in particular with cultural-evolutionary theory, in
which the cumulative character of human culture provides one ground for applying
Darwin’s idea of descent with modification1; and with approaches that regard
cumulative culture or so-called ‘ratchet’ effects as uniquely human.2 More particu-
larly, we focus on one standard way in which this cumulative character has been
presented, namely in terms of the complexity of cultural traits, where technologies are
taken as primary examples of such complex traits.3 Our leading question is whether,
irrespective of intuitive appeal, the empirical evidence is sufficiently strong to support
or undermine this technological complexity thesis, which holds that cumulative
technological complexity is a distinctive characteristic of human cultural evolution.
This question resembles a famous predecessor. In 1991, the biologist–philoso-
pher Daniel McShea published a now classic paper (McShea 1991, this journal),
titled ‘‘Complexity and evolution: what everybody knows?’’, in which he criticized
the consensus among evolutionists that (morphological) complexity increases in
biological evolution. Reviewing the little empirical evidence available, McShea at
that time reached the skeptical conclusion that the matter was far from decided; not
enough credible evidence existed to make an empirical case either for or against the
increasing complexity thesis.4 For example, in many cases McShea found that the
supposedly supportive evidence didn’t come from random samples, but was
deliberately chosen in order to make a case.
As to what sustained the consensus despite the lack of decisive evidence, McShea
(with Ruse 1988) conjectured that evolutionists might read into evolution the
progress that seems evident in technology and science. Yet, as McShea conceded,
perhaps received opinion on technology suffers from myopia just as much as
received opinion on biology. Perhaps we are, just like the evolutionists McShea
1 E.g., ‘‘Darwin famously described biological evolution as ‘descent with modification’. (…) We can
similarly demonstrate the gradual accumulation of modifications in culture. (…) [S]uccessful innovations
are always slight modifications of what went before, or the combination of previously separate
innovations’’ (Mesoudi 2011a, p. 33).2 ‘‘Human culture … has the distinctive characteristic that it accumulates modifications over time (what
we call the ‘ratchet effect’)’’ (Tennie et al. 2009: 2405).3 E.g., ‘‘Human culture … shows often astounding complexity, such as… technological artefacts such as
computers and space shuttles that are composed of countless functionally interlinked parts’’ (Mesoudi
2011a, p. ix).4 By 2010, McShea seemed to have become less skeptical, since in their book ‘‘Biology’s first law’’,
McShea and Brandon approvingly discuss a substantive body of recent work that might confirm the
complexity thesis (McShea and Brandon 2010, p. 50).
1246 K. Vaesen, W. Houkes
123
discussed, biased towards certain types of data, such as contemporary Western
technologies, for which the technological complexity thesis seems self-evident.
In this paper, we show that the empirical case for the technological complexity
thesis is surprisingly weak. Moreover, we indicate the implications this finding has
on future theoretical and empirical work in the abovementioned research traditions
that have been significantly shaped by this thesis.
We start by showing in more detail how the technological complexity thesis has
been expressed in work in cultural-evolutionary theory (Sect. 2). In particular, we
argue that different versions of the thesis have been presented as stylized facts or
phenomena which cultural-evolutionary accounts or models are supposedly able to
account for.
Then, in Sect. 3, we clarify what an adequate test of the technological complexity
thesis should look like. Preferably, following McShea (1991), such a test would be
based on a representative sample of established lines of descent that contain
information on complexity (in any of the senses that have been proposed). We
explain the difficulties in applying these adequacy criteria to technological
evolution, but also point out reasoned strategies to overcome them.
Section 4 performs the test, relying on the few datasets that fit our purposes. We
find that the data do not unequivocally point to complexity increase or decrease and
that, even if they do, they do so only for very specific, and usually different,
conceptualizations of complexity. Furthermore, the data fail to form a representative
sample. In sum, the currently available evidence neither substantiates nor
undermines the idea of a general trend towards higher complexity in technological
evolution.
In Sect. 5, we draw out the implications for research in cultural-evolutionary
theory, more specifically, the body of literature discussed in Sect. 2. We observe
that comparative studies, in light of Sects. 3 and 4, fail to establish the accumulation
of technological complexity to be uniquely human; and we highlight how future
empirical work might address this issue. Furthermore, we explain that, in light of
Sect. 4, it is uncertain whether cultural-evolutionary models devised to provide
explanations for the evolution of complex culture target real phenomena. And,
again, we point out strategies modelers might pursue to accommodate this worry.
Also, we suggest that it might be worthwhile to operationalize cumulative culture in
terms other than complexity, e.g., in terms of diversity, adaptivity or efficiency.
In the concluding section (Sect. 6), we discuss the relation of our empirical
findings to theoretical work in biology, work that has suggested that complexity will
generally increase in biological evolution (Fisher 1986; McShea and Brandon
2010). We argue that this work is compatible with finding decisive evidence against
the technological complexity thesis. Additionally, we critically assess the ideas, also
prominent among cultural evolutionists, that humans are uniquely capable of
producing technologies which no single individual could invent alone and are
uniquely capable of developing complex technologies. We find that both ideas are
underdetermined by the available evidence.
Complexity and technological evolution: What everybody… 1247
123
Technological complexity and cultural evolution
In this section, we take two steps that are preliminary to analyzing the evidence for
the technological complexity thesis. Firstly, we review how advocates of cultural-
evolutionary theory have endorsed or alluded to versions of the technological
complexity thesis in programmatic statements and more specific (modelling) efforts.
Secondly, and relatedly, we disambiguate the thesis as it features in this body of
work: we distinguish different versions that require different types or measures of
evidential support and that also differ in their salience for cultural-evolutionary
theory. We find that, where McShea (1991) distinguished weak and strong versions
of the biological complexity thesis, endorsements of minimally five versions of the
technological complexity thesis may be found in the work of cultural evolutionists.
Cultural-evolutionary theory and variants such as gene-culture co-evolutionary
theory and dual-inheritance theory claim that human culture is an adaptation that is
pivotal in the remarkable evolutionary success of our species. Cultural traits are
regarded as transmitted between generations through imitation and social learning,
constituting an inheritance system that is interrelated with, but in principle
independent from genetic inheritance. Here, cultural traits are understood broadly,
as any information that is capable of being expressed in behavior and of being
transmitted through social learning; it includes languages, legal systems, religions,
artistic expressions, artisanal skills, and technologies. Much of the work within
cultural-evolutionary theory consists of modelling the population-level effects of
various known or supposed features of human cultural transmission—such as our
propensity to imitate frequently observed traits (conformity bias) or to mimic
perceived success (success bias). Such population-dynamic models of cultural
evolution should, for instance, underpin the adaptiveness of specific learning
strategies; explain why humans are unique among biological species in the depth
and scope of social learning; or explain various ‘stylized facts’ regarding human
evolution.
Technologies feature prominently in such stylized facts. We shall go on to
distinguish several claims made in cultural-evolutionary theory, which may be
understood as subtly different ways of developing a shared narrative or framework
concerning human evolution and the role of technology. This narrative is that
humans are unique among biological species in possessing a repository of traits
which have gradually accumulated over many generations of human learners, and
which are adaptive or functional, i.e., which facilitate or enable survival in a wide
variety of specific environments. Technologies are a, or perhaps the, prime example
of these products of human cultural evolution. To underline the importance of
intergenerational cultural transmission, most versions of the narrative stress that
technologies can not be the result of individual learning, because no single human
designer, or single generation of human designers, has the capacity to invent them
‘from scratch’. Typically, the intuition that technologies have progressed beyond
individual invention is captured by describing them as ‘complex’. Thus, the
narrative assumes, or makes plausible, that there is a process of ‘‘gradual cultural
evolution of complex, adaptive technologies’’ (Boyd et al. 2013: 120), that this
1248 K. Vaesen, W. Houkes
123
process and its products are supposedly unique for humans and casts them as a
central explanandum in cultural-evolutionary theory, or as a target phenomenon for
cultural-evolutionary models.
This basic narrative is compatible with a variety of more specific claims
regarding human cultural evolution and complex technologies. Here, we distinguish
five versions of what we call the ‘technological complexity thesis’. Although some
are more explicitly endorsed in the literature than others, each version may be
illustrated with one or more quotes or paraphrases. These also illustrate that, in
outlining the phenomenon, technology is predominantly described as ‘complex’,
and only occasionally—often in conjunction—in other terms such as ‘diverse’,
‘efficient’ or ‘adaptive’. The five versions are:
(a) ‘Presence’: human cultural evolution has resulted in complex technologies.5
(b) ‘Threshold’: human cultural evolution has resulted in technologies that are so
complex that they cannot be the result of individual learning.6
(c) ‘Overall increase’: human cultural evolution gives rise to, globally, ever more
complex technologies—although there might be occasional, local decreases
in complexity.7
(d) ‘Monotone increase’: in each successive generation, human cultural evolution
results in more complex technologies.8
(e) ‘Numerically tractable increase’: human cultural evolution results in
technologies that increase in complexity over successive generations in some
numerically tractable pattern, e.g., linearly, or exponentially.9
5 E.g., ‘‘the cultural evolution of human technology is similar to the genetic evolution of complex
adaptive artifacts in other species, things like bird’s nests and termite mounds’’ (Boyd et al. 2013, p. 120).6 E.g., ‘‘Individuals are smart, but most of the cultural artifacts that we use … are far too complex for
even the most gifted innovator to create from scratch’’ (Richerson and Boyd 2005, p. 53); ‘‘highly
complex adaptations arise by cultural evolution even though no single innovator contributes more than a
small portion of the total’’ (Boyd et al. 2013, p. 122); ‘‘Human cumulative culture combines high-fidelity
transmission of cultural knowledge with beneficial modifications to generate a ‘ratcheting’ in
technological complexity, leading to the development of traits far more complex than one individual
could invent alone’’ (Dean et al. 2014: p. 284); ‘‘human culture [provides] knowledge accumulated across
many generations that an individual alone could never produce’’ (Tennie et al. 2009: 2411).7 E.g., ‘‘The tools essential for life … evolve, gradually accumulating complexity through the aggregate
efforts of populations of individuals, typically over many generations’’ (Boyd et al. 2013, p. 120); ‘‘a
characteristic feature of human cultural evolution is the seemingly limitless appearance of new and
increasingly complex cultural elements’’ (Enquist et al. 2011); ‘‘only humans appear to possess
cumulative culture, in which cultural traits increase in complexity over successive generations’’ (Kempe
et al 2014: 29; abstract); ‘‘Of course, cultural evolution may also lead to a decrease in the efficiency,
amount and complexity of culture’’ (Enquist and Ghirlanda 2007: 48).8 E.g., ‘‘the modification, over multiple transmission episodes, of cultural traits resulting in an increase in
the complexity or efficiency of those traits’’ (Dean et al. 2014: 287); ‘‘but human culture appears unique
in that it is cumulative, i.e. human cultural traits increase in diversity and complexity over time’’ (Lewis
and Laland 2012: 2171). This monotone increase is also suggested by labelling the gradual accumulation
of cultural complexity the ‘ratchet effect’. E.g., ‘‘This ratcheting up in the complexity of cultural traits,
frequently across multiple generations, has been proposed to be the hallmark of human culture’’ (Dean
et al. 2014: 285).9 E.g., ‘‘Why does human culture increase exponentially?’’ (Enquist et al. 2008; title); ‘‘the original
model exhibits a linear increase in cultural complexity continuing to infinity’’ (Mesoudi 2011b: 5).
Complexity and technological evolution: What everybody… 1249
123
This multiplicity of versions prompts the question which is the most salient in the
context of the explanatory project outlined above. For this, we should note firstly
that not each of these versions is endorsed as identifying a unique feature of human
evolution; tellingly, the illustrative quote for thesis (a) stresses the commonality
with nonhuman species, whereas some of those for theses (c) and (d) claim
humaniqueness. Furthermore, it is readily apparent that theses (d) and (e) are
stronger versions of, i.e., logically entail, thesis (c). Thesis (e) may be regarded as,
in turn, stronger than thesis (d), since the latter does not involve any claim regarding
numerically tractable patterns in complexity increase. However, many actual
endorsements of thesis (e) are explicitly limited to some technologies, and come
with the acknowledgement that, locally, the complexity of some—or, in rare cases,
even all—technologies may decrease. Thus, we take thesis (d) as a qualitatively
stronger version of thesis (c), and thesis (e) as a quantitatively stronger version.
Similarly, the relation between theses (b) and (c) is more intricate than it might
appear. At first glance, claiming the former may seem less demanding than claiming
that cultural evolution more or less continuously increases technological complex-
ity. However, whereas thesis (c) can in principle be substantiated by choosing an
appropriate measure of complexity, thesis (b) requires a clear specification of some
threshold value of whatever measure of complexity is chosen to substantiate thesis
(c).10
Given these relations between the five versions and their salience for cultural-
evolutionary theory’s claims regarding humaniqueness, a test of the technological
complexity thesis can reasonably start by inspecting how the ‘overall increase’
thesis (c) may be substantiated (Sects. 3 and 4), before turning to the support for one
of the other theses (Sect. 6).
To substantiate any version of the technological complexity thesis, several
strategies are in principle available to cultural evolutionists. One is to draw on
analogies between biology and culture in order to make credible a technological
counterpart of a biological complexity thesis (more on this in Sect. 6). Another is to
offer theoretical, e.g., model-based arguments. Perhaps surprisingly, given the
frequent use of analogical arguments in support of the application of Darwinian
principles and the strong orientation towards modelling, neither of these argumen-
tative strategies is used by cultural evolutionists to substantiate any version of the
complexity thesis as an explanandum. Rather, the thesis is presented as a stylized
fact on the basis of a few salient examples, such as smartphones and space shuttles.
Now, it may well be the case that there is a strong empirical basis for the
technological complexity thesis, which undergirds its intuitive appeal and from
which a wide variety of illustrative examples could be drawn. Our question in the
next two sections is: is there sufficient and appropriate empirical evidence to
accept—or, alternatively, to reject—the ‘overall increase’ version of the techno-
logical complexity thesis?
10 This does not mean that thesis (c) entails (b). In principle, technologies could have been too complex
for individual invention from the dawn of humankind, so that human cultural evolution has not strictly
speaking resulted in such technologies. There is a strong suggestion in cultural-evolutionary work,
however, that technologies started out simple enough to be invented from scratch.
1250 K. Vaesen, W. Houkes
123
Adequacy criteria for a test of the technological complexity thesis
According to version (c) of the technological complexity thesis, complexity
increases globally or on average, that is, in some lineages and more (often) than not.
Accordingly, following McShea (1991), an adequate test of the thesis is one that is
based on a representative sample of established lines of descent for which the
complexity of ancestors and descendants has been or can be measured.
In order to apply these adequacy criteria to technology, they need to be made
more precise. To start, what would count as an acceptable measure of technological
complexity? In many presentations, cultural evolutionists have understood
technologies straightforwardly as functional artifacts, such as kayaks, bows, and
dogsleds (Boyd et al. 2013, p. 122). Other versions include or focus on bodies of
knowledge (e.g., Mesoudi 2011b) rather than material items; and still others refer
more or less consistently to behavioral patterns (e.g., Dean et al. 2014). This reflects
a multiplicity of meanings that is widely acknowledged in the philosophy of
technology (see, e.g., Mitcham 1978): ‘technology’ may refer to collections of
artifacts, to bodies of knowledge, or to goal-directed practices, where none of these
meanings holds obvious conceptual priority over the others. This multiplicity also
surfaces in the various attempts to define technological complexity. As can be seen
in Table 1, some definitions pertain to technological artifacts (definitions [1–2]),
some to technological behavior (definitions [3–6]), others can be applied to both
artifacts and behavior (definitions [7–9]). At a first pass, any sensible operational-
ization of the items on the list might be considered adequate. Yet, one might also
insist that some measures are better than others. For instance, it has been claimed
that humans’ unique capacity for cumulative cultural evolution can be attributed to
imitation, i.e., faithful replication of the actual behavior of a mentor (e.g.,
Tomasello and Call 1997; Tomasello 1999; Boyd and Richerson 1996; Richerson
and Boyd 2008; Mesoudi et al. 2013), rather than to emulation, i.e., re-invention of
behaviors as a response to being exposed to the end products (e.g., artifacts) of
others’ behaviors. If this is right—we take no stand on this issue here—and if
cumulative culture refers to the accumulation of technological complexity (Mesoudi
2011a, b; Boyd et al. 2013; Kempe et al. 2014; Dean et al. 2014), the most natural
definition of complexity would thus be one that pertains to technological behavior
(definitions [3–6]) rather than to technological artifacts (definitions [1–2]).
One might think that the latter type of complexity is a reliable proxy for the
former type. But this is far from established. Artifacts are multiply realizable. For
example, prehistoric projectile points were made from stone or from osseous
materials, such as bone, antler and ivory. Osseous points seem at least as complex
as, if not more complex than, stone points: they have the same number of techno-
units (definition [1]), also when hafted; when hafted, the densities of interactions
between components plausibly are identical (artifactual definition [7]). However,
since osseous materials are less subject to breakage, points made of caribou antler,
are probably easier to produce than stone tools (Guthrie 1983) (behavioral
definitions [3], [4] or [5]). More specifically, they can be produced by gradually
scraping the raw material into the requisite form, and thus do not depend on
Complexity and technological evolution: What everybody… 1251
123
preparatory work that is as extensive and risky as the work required for producing
stone points.
In a similar vein, one cannot read off from an artifact the complexity of its
usage—indeed, another type of technological behavior that is subject to imitation.
For instance, because automatic gearboxes contain more components (definition [1])
and perhaps denser interactions between parts (definition [7]), they would seem
more complex than manual gearboxes. But the reverse holds when we consider their
use. The series of actions involved in operating a manual gearbox includes one
component more than the series of actions involved in operating an automatic
transmission (definition [8]), i.e., gear-shifting, and that component interacts with at
least one other component in the series (definition [7]), i.e., steering the wheel.
Measures of complexity might even diverge within their own class (i.e.,
artifactual or behavioral). Complexity of production is not a reliable indicator of
complexity of usage, as illustrated by the gearbox example. Or, to take an example
from archaeology, unbarbed spears made out of one piece of wood—as those found
at Schoningen (Thieme 1997)—are relatively easy to produce, but it requires an
awful amount of skill and knowledge about the prey and the environment to put
these one-component tools to effective use. Finally, obviously, there is no reason to
Table 1 Definitions of technological complexity
Nr. Definition of technological complexity Applies to References
[1] Complexity expressed as the number of techno-
units an artifact consists of; techno-units are the
different kinds of parts in a tool
Artifacts Oswalt
(1973, 1976)
[2] Complexity expressed as the number of tools in a
toolkit
Artifact sets Oswalt
(1973, 1976)
[3] Complexity expressed as transmission
inaccuracy, i.e., the inaccuracy of learning a
trait from a mentor; complex skills are those
that are hard to learn, and thus have high
transmission inaccuracy
Behavior Henrich (2004) and
Powell et al.
(2009)
[4] Complexity expressed as skillfulness Behavior Mesoudi (2011b)
[5] Complexity expressed as the presence of multiple
sets of (inexact) means-end connections
Behavior March and Simon
(1958)
[6] Complexity expressed as the presence of
interrelated, conflicting subtasks
Behavior Campbell (1984)
[7] Complexity expressed by the density of
interactions between a system’s parts
Undefined; can be
applied to artifacts and
behavior
Simon (1962)
[8] Complexity expressed as a function of the number
of parts, the degree of differentiation or
specialization of these parts, and their
integration
Undefined; can be
applied to artifacts and
behavior
Service (1962)
[9] Kolmogorov complexity of an object (e.g., a use
plan) expressed as the length of the shortest
description of that object
Undefined; can be
applied to artifacts and
behavior
Kolmogorov
(1974)
1252 K. Vaesen, W. Houkes
123
expect artifactual definitions [1] and [2] to be correlated. The number of techno-
units of a hafted spear tells us little about the number of other tools within the spear
user’s toolkit.
Such possible divergence between measures of complexity is also relevant to
specifying what would count as a representative sample for testing the complexity
thesis. Version (c) of the complexity thesis comes in at least as many variants as the
number of definitions in Table 1. Said divergence now tells us that for a sample to
be representative it ought to match the variant under consideration.
What else would make for a representative sample? Since the accumulation of
complexity is supposed to be uniquely human, the sample must preferably be drawn
from the population of all past and present human technologies. Supposing we have
agreed on a measure of complexity, we also must get a sense of whether that
population contains subpopulations or strata, each of which would need to be
sampled independently for the sample to be representative. Biological taxa usually
serve the purpose of stratification in biology, but unfortunately no such established
strata are available for technology. So whereas we might draw a subsample from all
the large branches of the tree of life, we lack a tree of technology, or any other fully
comprehensive classification with non-overlapping classes, allowing us to do the
same for technological evolution. Consequently, we will need to perform a fully
random sample, or else define strata which we, on theoretical grounds, suspect to be
homogeneous in the relevant respects. Regarding such stratification, the level of
technological complexity among small-scale societies has been claimed to depend
on a variety of factors: environment, resource availability, subsistence, sedentism,
linear settlement, technology, storage, population, exchange, conflict, competition,
social organization, territoriality, style, labor organization, craft specialization,
inequality, and status differentiation (Price and Brown 1985). These factors thus
might guide us in subsample selection. Ideally, our sample would include a decent
amount of variation with respect to these variables. If, as will turn out later, this
proves unfeasible, the next best option is to seek and sample the extremes. One
might for instance sample hunter-gatherer and WEIRD (Western, Educated,
Industrialized, Rich and Democratic; Henrich et al. 2010) societies, as they, even
taking into account internal variability, might be taken to differ substantially in a
substantial number of respects, such as environment, resource availability, and
sedentism. Alternatively, one might contrast samples pertaining to selectively
neutral traits with samples pertaining to traits that are under intense selective
pressure; functional considerations might be thought to constrain trends in
complexity in the latter but not in the former.
Although such stratifications may avoid one of the problems which the lack of a
well-established phylogeny of technologies gives rise to, it does not help us to
address a second, thornier issue. The requirement to start from well-established lines
of descent implies that one must consider, where possible, the developments in all,
or at least an appropriate subset, of the lineages branching off from a given ancestor.
To appreciate the importance of this point, consider Fig. 1, which is Steven
Mithen’s (1996) fairly standard reconstruction of technological development over
the history of our species. It starts with the first flaked stones documented by the
archaeological record, dating to 2–3 million years ago, and continues until the
Complexity and technological evolution: What everybody… 1253
123
Fig. 1 Mithen’s reconstructionof human technologicalevolution (redrawn from Mithen1996)
1254 K. Vaesen, W. Houkes
123
emergence of the present-day computer. Now even if we were to agree with Mithen
that the sequence evidences increasing complexity, and even if we assume that it
represents actual ancestor-descendent relations (which evidently is contentious), the
sequence does not establish the complexity thesis. For it includes only the tools that
are supposedly diagnostic for the acts in what Mithen calls the drama of our past. So
the Oldowan (2–1.5 Ma) (Act 2) is taken to be typified by the sharp-edged stones
produced by striking one cobble (the core) with another (the hammerstone), as well
as the remaining cores, which can be variously used as a chopper or scraper or
something else. The emblematic feature of the early Acheulean techno-complex (ca.
1.6–0.5 Ma) (Act 3) is considered to be the bifacial handaxe, produced by more
obviously shaping a core by means of both stone and soft hammers, whereas the late
Acheulean (0.5–0.25 Ma) is typified by the emergence of Levallois tools, i.e., stone
points the size and form of which is predetermined by careful preparation of the
core. Mithen’s reconstruction thus ignores the diversity of tools within each act. For
example, it ignores the fact that flaked tools continue to be abundantly present in the
Acheulean and in fact the whole of prehistory, much later, e.g., among
contemporary Aboriginals (Holdaway and Douglass 2011) and, until the 1960s, in
the Mediterranean (Karimali 2005); it ignores other marked continuities, such as the
persistent use of bone implements to work animal hides (Soressi et al. 2013) and
unbarbed spears made out of one piece of wood—as at Schoningen (Thieme 1997)
and among recent populations, for example in New Guinea and Australia (McCarthy
1957)—from the Pleistocene until present times; it ignores the fact that later stone
implements, such as some of the handstones used during the Neolithic for grinding
cereals, have a production process comprising only one step (i.e., seeking a
suitable piece of rock; ibid.), which makes them, per definition [8], simpler than
Oldowan tools; it ignores the fact that, as already hinted at, the use of osseous
materials might have actually simplified production processes; and so forth.
Obviously, these examples are insufficient to undermine the complexity thesis. Yet
they do illustrate that uni-linear reconstructions fail to record developments which
might shift the evidential balance, and thus which any proper test would like to
include.
One might worry that technological evolution is highly reticulate, and therefore
that the demand to start from established lines of descent is too stringent. Indeed,
our ability to produce unambiguous rooted networks is currently limited (Morrison
2011). So perhaps we should acknowledge that, currently, we often lack the means
to reliably infer technological ancestor-descendent relationships. This acknowl-
edged, the next best option is to draw diachronic samples from a domain that
preferably is wider in range than that of uni-linear reconstructions. The underlying
assumption would be that, if the complexity thesis is true, one should observe an
increase in complexity irrespective of the precise evolutionary relations between the
diachronic samples.
Complexity and technological evolution: What everybody… 1255
123
Testing the technological complexity thesis
To our knowledge, and for the moment ignoring the question of representativeness,
no dataset meets the adequacy criteria discussed above. Recent work on cultural
evolution has produced a number of rooted phylogenetic trees, e.g., relating to
prehistoric projectile points (O’Brien et al. 2001), musical instruments (Temkin and
Eldredge 2007), tribal textiles (Tehrani and Collard 2009), cutlery (Riede 2009),
bicycles (Lake and Venti 2009) and canoes and houses (Jordan 2014). Yet none of
these reconstructions contains direct information on complexity, nor is it obvious
how one would infer such information. Hence, even if one grants that they
accurately represent evolutionary relationships, they do not provide an appropriate
test of the complexity thesis. The same holds for uni-linear sequences developed for
ship rudders (Mott 1997), the cotton gin, steam engine and barbed wire (Basalla
1988), pencils (Petroski 1989), and paperclips and zippers (Petroski 1992). Some
recent experimental studies provide evidence for the effect of population size on
maintaining a simple or complex technology (Derex et al. 2013), where producing
the complex technology requires more steps (definition [8]), but do not concern
increasing complexity.
Uni-linear sequences that do contain information on complexity include
sequences developed for early stone tools and for software packages.
Regarding the former, Perreault et al. (2013) calculated the complexity of some
of the diagnostic stone tools from assemblages from the Lower to Upper Paleolithic,
by counting the procedural units involved in tool manufacture (definition [8]). The
authors defined a procedural unit as a manufacturing step that makes a distinct
contribution to the finished form of a technology (e.g., heat treatment, decortifi-
cation, hard hammer percussion). Perreault et al. found that the average number of
procedural units increased over time. In another study, Stout developed action
hierarchies for various forms of stone tool production, including the production of
Oldowan and Early Acheulean flakes, and Early and Late Acheulean handaxes.
Consider Fig. 2 (adapted from Stout 2011), which presents action hierarchies for
Oldowan and Late Acheulean flake detachment. The latter is more complex than the
former for two reasons (Querbes et al. 2014). First, it has more constitutive elements
(definition [8]). Second, these elements are organized in a hierarchical structure that
comprises more nested levels: the addition of platform preparation to the
superordinate goal of percussion in Late Acheulean flake detachment introduces
four nested levels, so that the method contains six nested levels in total (versus four
in Oldowan flake detachment). The success of the superordinate level (i.e.,
percussion) is thus contingent on four elements (rather than three, as in Oldowan
production), namely position core, hammerstone grip, strike plus platform
preparation, and the success of the latter is itself contingent on the interplay of a
whole set of lower-level actions. Given this nestedness, Late Acheulean flake
detachment is more complex in that it exhibits a higher density of interactions
between individual actions (definition [7]); even a very small error introduced
during transmission in one element may have profound repercussions on the
performance of other elements, and thus on success overall. In sum, Perreault et al.’s
1256 K. Vaesen, W. Houkes
123
uni-linear sequence provides support for the complexity thesis in one of its variants
(i.e., corresponding to definition [8]), whereas Stout’s sequence provides support for
two of the thesis’ variants (i.e., corresponding to definitions [8] and [7]).11
In computer science, complexity is considered to be a vice rather than a virtue;
the more complex a software package, the more likely it contains a bug and the
higher its maintenance costs (Sessions 2008). Hence, computer scientists have spent
quite some effort on understanding the sense in which complexity might increase in
Fig. 2 Action hierarchies for a Oldowan flake detachment; b Late Acheulean flack detachment (redrawndetail from Stout 2011). Lines connect subordinate levels with the superordinate element they instantiate.In b, dashed lines indicate action chunks which are identical to those defined in a
11 Some authors (Shott 2003 and references therein) have argued that stone tool reduction sequences, as
the ones developed by Perreault et al. (2013) and by Stout (2011), contain an unacceptable level of
arbitrariness. Such sequences would thus be inadequate for the purpose of testing the complexity thesis.
We ignore this complication, however, and rather try to present the strongest case for the complexity
thesis. To anticipate Sect. 4, we will find that the support for the complexity thesis is weak, even without
having to invoke methodological issues of the sort raised by Shott and others.
Complexity and technological evolution: What everybody… 1257
123
software evolution. Empirical studies seem to agree that software generally grows
over time (see the review in Neamtiu et al. 2013). This trend is attested by increases
both in the numbers of lines a software package comprises (definition [1] and [9])
and in the number of modules contained in a software package (definition [1]). With
regard to measures which computer scientists take to be genuine indicators of
complexity, the picture is mixed. There is some evidence that interface complexity,
which is a function of the number of input parameters to a function and the number
of output states from the function (plausibly, definition [1]), might be decreasing
(Bhattacharya and Neamtiu 2011). Common coupling, which is a metric for the
strength of relationships between functional units (such as modules), might increase
in absolute terms, but not when normalized over the total number of possible
couplings (Neamtiu et al. 2013). Note that since according to definition [7] it is the
density rather than the sheer number of interactions that counts for something to be
complex, it is the trends in normalized common coupling that provides a suitable test
of the complexity thesis (i.e., understood in terms of definition [7]). The available
evidence for so-called cyclomatic complexity also is mixed. Cyclomatic complexity
is measured by the number of independent paths through a program’s code. For
instance, if a source code contains no conditionals, such as if-else statements, there
is only one path from start to end, and the code’s cyclomatic complexity would be
one. If it contained one such conditional, there are two possible paths through the
code, and cyclomatic complexity would be 2. Cyclomatic complexity thus
resembles closely the multiplicity of means-end connections that is central to
definition [6]. It has been found to increase (Neamtiu et al. 2013) and decrease
(Bhattacharya and Neamtiu 2011) in absolute terms; when normalized, cyclomatic
complexity does not appear to increase (Neamtiu et al. 2013).
One possible reason for the lack of unambiguous evidence for complexity increase
might be that software developers are sensitive to the risks such increases pose (i.e.,
more bugs, higher maintenance costs), and thus at least attempt to ‘design for
simplicity’. A similar strategy is pursued in other industries, usually under the banner
of ‘design formanufacture’. Here, the idea is to design products in such away that their
production is easy and cheap. Consider a case presented by Corbett et al. (1991; see
also Vaesen 2011). It concerns the design of simple scales, used in the retail business.
In 1983, W&T Avery Ltd. introduced the Avery 1770 (see Fig. 3, left), a scale that
soon could be seen in shops throughout the UK. Although one expected world market
volumes to grow, the company also expected competition to increase. Therefore it
immediately started to redesign the 1770, explicitly bearing ease of manufacture in
mind. The effort led in 1986 to a new scale, the A600 (see Fig. 3, right). The scales
were functionally equivalent, but in the A600 the number of components was reduced
substantially (see Fig. 3) (definition [1]). This, in turn, reduced assembly time and
created benefits relating to production control, timely delivery, quality, investments,
the supply chain and inventory of parts and final products. In a similar vein, in order to
reduce production costs engineers might ‘design for modularity’, i.e., produce designs
which limit or cluster interactions between components (definition [7]) (Baldwin and
Clark 2000). In sum, engineers have certainly understood that complexity of
production processes and finished products comes at a price and, accordingly, have
come up with various strategies for simplification.
1258 K. Vaesen, W. Houkes
123
Another class of studies does not track ancestor-descendent relationships, but
compares the complexity of elements within two or more diachronic samples. We
know of three such studies. In a first study,Moore et al. (2009) examined the stone tool
assemblages found in five stratigraphic layers (spanning some 95 k years, viz.*100
to*5 kya) from a cave in West Flores. Moore et al. drew a large sample of artifacts
from each stratigraphic layer (11,667 artifacts in total) and counted the type and
number of percussion blows needed to produce the artifacts. The authors observed that
the median number of blows (definition [8]) across the assemblages remained
constant. This is not to say that during the Holocene, as evidenced by the latest of the
assemblages, no changes occurred: there was a higher reliance on unifacial flaking, the
appearance of edge-glossed flakes, a change in raw material selection and more
frequent fire-induced damage to stone artifacts. However, Moore et al. did not argue,
nor provided any reasons to assume, that these changes mark increasingly complex
modes of production: unifacial flaking and changes in raw material selection are
presented as simply different modes of production; edge-glosses are plausibly due to
characteristics of the material (viz. chert) and to usage; and there is no evidence that
fire-induced damage is the result of deliberate heat treatment. To be sure, elsewhere on
Flores Holocene deposits often contain edge-ground, rectangular-sectioned adzes,
artifacts which Moore et al. claim (but do not demonstrate) to be more complex. So
future study might very well reveal that stratigraphic layers containing such adzes are
more complex (as measured by the median average of blows needed to produce the
artifacts within the stratigraphy) than Pleistocene deposits. At present, however, no
such evidence is available.
Fig. 3 The Avery 1770 (left) and the Avery A600 (right), redesigned for ease of manufacture (redrawnfrom Corbett et al. 1991)
Complexity and technological evolution: What everybody… 1259
123
In a second study, Lindenfors et al. (2015) examined the development of
European cooking recipes from medieval to modern times, and found evidence for
increasing complexity. Over time, recipes documented by cookery books for
instance tended to involve more steps (definition [8]) and a wider variety of
techniques (possibly, definition [2] or [5]), and dishes tended to contain more
ingredients (definition [1]).
Finally, third, Strumsky et al. (2012) conclude, based on patent claims from the
period 1835–2005, that the patent record documents growing complexity of
technological inventions. This conclusion is inferred from the observation that
patents, over time and on average, tend to be assigned to a higher number of patent
codes, and this might be taken to imply that patented technologies comprise a higher
number of constitutive elements (definition [1]). To illustrate the relationship
between the number of codes and the number of constitutive elements of a patented
invention, consider one of the examples provided by Strumsky et al. Patent number
6,823,725 (filed on 12 December 2000 and granted on 30 November 2004) concerns
a ‘Linear distance sensor and the use thereof as actuator for motor vehicles’. This
patent is categorized by three codes, namely 73/114.01, 324/207.13 and 324/207.24.
Code 73/114.01 implies that the invention involves a process or apparatus to
perform a specific test on combustion engines; code 324/207.13 indicates that it
involves a process or apparatus for measuring changes in a magnetic field; and code
324/207.24 specifies that it involves a process or apparatus for measuring motion in
a straight line. This example, as well as the other examples in the study by Strumsky
et al. (see their Appendix I), thus suggest that patent codes might be a reasonable
proxy for the number of elements a patented invention comprises.
Table 2 summarizes our review. Recall that in order to assess the support for the
complexity thesis, we must assess the support for specific variants of version (c) of
the complexity thesis. We now observe that, for most of these variants, we have
either no evidence (variants [3] and [4]), evidence pertaining to only one
technological domain (variants [2], [5], [6] and [9]), or evidence pertaining to
two technological domains ([7], [8]), one of which is ambiguous in its support. The
broadest sample is available for variant [1]. But also here the sample includes one
Table 2 Overview of results
(?, results supporting the
complexity thesis; ±,
ambiguous results)
Def. nr. Technological domain
Stone tools Software Cooking recipes Patents
[1] ± ? ?
[2] ?
[3]
[4]
[5] ?
[6] ±
[7] ? ±
[8] ± ?
[9] ?
1260 K. Vaesen, W. Houkes
123
domain (out of three) with ambiguous results. Surely, finding one ambiguous result
in a representative sample would not invalidate a thesis according to which
technological complexity can be expected to increase on average. Yet, the fact that
the sample for variant [1] is not representative—it is a WEIRD sample, which might
furthermore inadequately represent some tendencies within WEIRD technologies,
such as a trend towards design for simplicity, manufacture or modularity—does
allow us to conclude that what McShea observed in biological evolution also applies
to technology: insufficient credible evidence exists to make a forceful empirical
case either for or against the complexity thesis.
Implications for research on cumulative culture
A recent paper by Dean et al. reviews for various species the evidence for
cumulative culture, which the authors characterize as ‘‘the modification, over
multiple transmission episodes, of cultural traits resulting in an increase in the
complexity or efficiency of those traits’’ (Dean et al. 2014, p. 287, italics added).
From an early study by Lehman (1947) and a study by Basalla (1988) Dean et al.
infer that ‘‘human culture is clearly cumulative’’ (Dean et al. 2014, p. 287). For
other animals (chimpanzees, capuchin monkeys, macaques, crows), the authors
argue, the evidence is inconclusive.
Note that, irrespective of our previous arguments, the studies by Lehman (1947)
and by Basalla (1988) fail to support the complexity thesis. Lehman (1947) counted
the ‘‘outstanding contributions’’ in eleven academic fields (chemistry, genetics,
geology, mathematics, and so forth) and plotted them over time. Unsurprisingly, the
number of outstanding contributions started to rise at the onset of the scientific
revolution. Basalla (1988) examined the evolution of technologies such as steam
and internal combustion engines, the transistor, lighting systems, and the electric
motor, and argued that all of these technologies were developed by building on
previous inventions. Crucially, neither the observation of exponential growth of
outstanding contributions (Lehman) nor the observation of novel artifacts arising
from antecedent artifacts (Basalla) tells us that complexity increases in human
cultural evolution.
Section 4 provides an additional reason for not understanding cumulative culture
in terms of increased complexity. For the available evidence reviewed there appears
inconclusive, perhaps as inconclusive as Dean et al.’s evidence for non-human
animals. Furthermore, Sect. 3 presented an argument which casts doubt on how
Dean et al. compare humans and non-human animals. Assuming that the studies by
Lehman (1947) and Basalla (1988) actually did document increasing complexity,
they would do so for end-products, whereas most of the evidence for non-human
animals pertains to behavioral patterns (for chimpanzees: nut-cracking, ectoparasite
manipulation, digging of wells; for capuchins: various social games; for macaques:
stone-handling). Only the evidence Dean et al. (2014) discuss for New Caledonian
crows’ leaf tools matches the type of evidence they discuss for humans. In sum,
regardless of what the individual studies on humans and non-humans conclude,
Complexity and technological evolution: What everybody… 1261
123
Dean et al.’s comparison is too uneven to establish whether and in which sense
humans are unique.
Future work on the humaniqueness of cumulative culture would therefore seem
to have three options. The first is to operationalize cumulation in terms other than
complexity. For example, it might be less difficult to substantiate the claim that,
generally, human culture develops by modifying or recombining previous inven-
tions, or the claim that technological evolution tends towards increasing diversity
(see e.g., Shea 2011). The evidence reviewed in Sect. 3 at least seems compatible
with these claims, and other relevant evidence might be easier to come by.
A variant of this first option is suggested by the abovementioned characterization
of cumulative culture as ‘‘an increase in the complexity or efficiency of those traits’’
(Dean et al. 2014: 287, italics added) or, relatedly, as a ‘‘gradual cultural evolution
of complex, adaptive technologies’’ (Boyd et al. 2013: 120, italics added). Whereas
it might be difficult to substantiate increases in adaptivity, some studies (e.g., Morris
2013 and references therein) indeed suggest that humans might have become
increasingly efficient in capturing energy in the form of food, fuel and raw
materials.12
Note that shifting the focus from complexity to diversity or efficiency (or
adaptivity) is more than a terminological manoeuvre, since it entails a shift in the
types of evidence one should be looking for. While counting techno-units might be
relevant to substantiating the complexity thesis, it is irrelevant to testing increasing
efficiency. Consider the harpoons used by contact-era Inuit to hunt seals in open
water (Vaesen et al. 2016). Such harpoons typically had floats attached to them to
make it more difficult for the seal to dive. Adding an extra float would increase the
harpoon’s number of techno-units, but would decrease its level of energy capture
(sensu Morris), because the object would be more difficult to aim. In sum, it makes
good sense not to lump together concepts such as complexity and diversity and
efficiency, just as it appears to have made good sense not to lump together in
Sects. 3 and 4 various operationalizations of the notion complexity.
A second option for future work on humaniqueness of cumulative culture is to
retain the focus on complexity, but to try and match the evidence pertaining to
humans to the evidence pertaining to non-human animals. Given the fact that studies
on non-human animals have predominantly focused on behavior, future work on
humans should do the same—and, unlike recent transmission-chain experiments
(Caldwell and Millen 2008, 2010), should do so with the aim of demonstrating
increasing complexity rather than effectiveness.
A third option is the reverse of the second, i.e., trying to find evidence in non-
human animals for the patterns observed in humans (see Sect. 3). We have seen,
however, that these patterns are poorly documented, so that it is unclear what
precisely one should be looking for in non-human animals.
The considerations presented in Sects. 2, 3 and 4 also have implications for
cultural-evolutionary models devised to understand which factors might drive the
12 We thank an anonymous reviewer for pointing this out to us. Note, also, that adaptive complexity, a
notion that cultural evolutionists often allude to, in fact refers to two separate qualities (complexity and
adaptivity) rather than to one. So an adaptive, complex trait is a trait that is both complex and adaptive,
not one that is, conjunctively, adaptively-complex.
1262 K. Vaesen, W. Houkes
123
evolution of complex culture. Such models differ considerably in how they construe
complexity. Characterizations have been given in terms of definition [1] (e.g.,
Lehmann and Wakano 2013; Querbes et al. 2014), in terms of definition [3] (e.g.,
Henrich 2004; Powell et al. 2009; Vaesen 2012), in terms of definition [7] (Querbes
et al. 2014) and in terms of definition [8] (e.g. Lewis and Laland 2012). Given the
poor support for the complexity thesis (see Sect. 4), it is unsure whether any of the
said models actually target real phenomena. Support for the definition assumed by
the models of Lehmann and Wakano (2013) and Querbes et al. (2014) (namely
definition [1]) seems strongest; yet even here we found only three supportive cases
(i.e., cooking recipes, software evolution, patents), all coming from WEIRD
societies.
Modellers may pursue three strategies to accommodate the concerns raised here.
The first is, again, to operationalize cumulation differently (e.g., in terms of
diversity, efficiency or adaptivity). The second is to try to establish, empirically, that
the type of complexity increase targeted by their models is real. The third and final
strategy is to perform robustness analyses, i.e., test whether models yield similar
results under varying characterizations of complexity. By way of illustration,
consider a study by Querbes et al. (2014). The authors assessed two models
(developed by Henrich 2004; Powell et al. 2009) according to which demography is
a key driver of cultural complexity. Whereas these models assumed complexity to
be captured by transmission inaccuracy (definition [3]), Querbes et al.’s adaptation
of the models assumed complexity to be a function of the density of interactions
between functional parts (definition [7]). Querbes et al. found that, under definition
[7], demography plays a much more limited role in sustaining the accumulation of
complexity. Consequently, since empirical evidence is not sufficiently abundant to
prefer one of the two definitions (see Sect. 4), the models do not allow us to infer
that demography is a key driver of complexity. Conversely, convergence of results
under a wide variety of definitions would (!) increase our confidence in the
demography hypothesis.
Discussion
The evidence for and against version (c) of the technological complexity thesis (i.e.,
‘overall increase’) appears to be scarce and inconclusive. This finding is hard to
reconcile with what many of us believe, often quite strongly, about progress in
science and technology.
So, where does this consensus view come from? McShea (1991) offers several
hypotheses regarding the origins of the consensus view in biology, and these seem
to be sensible also with respect to technology. Perhaps we possess a cognitive
algorithm that correctly outputs increasing complexity, but employs a conception of
complexity which differs from those used in current empirical work. Perhaps items
simply look more and more different from contemporary ones as we go further and
further back in time; if contemporary items are assumed to be complex, less familiar
items would mistakenly be judged simpler. Or perhaps we are biased towards a few
Complexity and technological evolution: What everybody… 1263
123
spectacular cases (e.g., the Apollo mission), and accordingly get the impression of a
universal, long-term trend.
To the extent that one finds plausible the analogy between biological and
technological evolution, our findings are also hard to reconcile with theoretical work
in biology. This work has suggested that even if complexity in every lineage follows
a simple random walk (i.e., complexity decreases are as likely as complexity
increases), and if there is a lower bound to complexity, the mean complexity of all
lineages will rise (Fisher 1986; McShea 1991, 1994); and it has suggested that in the
absence of constraints or forces, complexity will increase in a lawful manner
(McShea and Brandon 2010).
There are obvious reasons to mistrust the analogy between biological and cultural
evolution. McShea and Brandon (2010, p. 132), for instance, find it plausible that in
cultural evolution quite a few homogenizing forces are at work. Consequently,
trends in technological complexity plausibly will not conform to a law that is
developed for cases in which no forces are at work. Likewise, even if Fisher’s work
would theoretically establish the complexity thesis in biology, it would only apply
to technological evolution if none of the following defeating conditions obtains:
decreases being more likely than increases, decreases being larger in size than
increases, and there not being a lower bound to complexity. Since none of these
conditions can be ruled out empirically, theoretical work in biology certainly leaves
open the possibility of finding conclusive evidence against the complexity thesis in
technology.
What Fisher’s paper does show, however, is that complexity, at least
theoretically, can increase even in the absence of high-fidelity transmission. Even
when cultural learners never copy the exact behavior of their cultural parent (e.g.,
complexity increases and decreases have equal probabilities of 0.5), Fisher’s work
predicts that, in the absence of defeating conditions, complexity will increase.
Hence, from the mere fact that human culture is characterized by the accumulation
of complexity one should not infer, as some authors seem to do (e.g., Tomasello and
Call 1997; Tomasello 1999; Boyd and Richerson 1996; Richerson and Boyd 2008;
Mesoudi et al. 2013), that such accumulation is the result of imitation, i.e., faithfully
replicating actual behavior of a mentor.
An alternative for advocates of the technological complexity thesis is to abandon
version (c), and focus instead on versions (a) (i.e., ‘Presence: human cultural
evolution has resulted in complex technologies) and (b) (i.e., ‘Threshold’: human
cultural evolution has resulted in technologies that are so complex that they cannot
be the result of individual learning). As indicated above, at least one of these—
version (b)—is still associated with the salient explananda regarding humanique-
ness. Yet as we made clear in Sect. 2, endorsing version (b) does not reduce the
need for evidential support. On the contrary, it requires a non-arbitrary specification
of a threshold value—and version (a) requires clear application criteria for the labels
‘simple’ and ‘complex’. Moreover, as argued in the previous section, substantiating
claims of humaniqueness would require an unbiased test of non-human cultural
traits with regard to whichever application criteria or threshold value has been
established. Both McShea’s hypotheses concerning the origins of the consensus
view in biology and associations with the problematic distinction between
1264 K. Vaesen, W. Houkes
123
‘primitive’ and ‘advanced’ cultures give reasons for being cautious regarding
version (a). Although—or perhaps precisely because—the ‘presence’ claim seems
intuitively obvious, there is a real risk that the claim merely expresses perceptions of
readily observable but superficial differences, or ignorance on the part of the person
claiming complexity for her own cultural traits.
In this regard, operationalizing version (a) through the ‘beyond-individual-
invention-from-scratch’ version (b) may look like a promising way forward. For this
version, however, our discussion of Dean et al. (2014)’s claims regarding
humaniqueness points out some of the complications: as obvious as (b) might
seem for Jumbo Jets or smartphones, comparison between human and non-human
culture needs to be on an equal (i.e., artifact vs artifact or behavior vs behavior)
footing. Moreover, and in closing, the evidential basis for (b) should not be
overrated. For most technologies, re-invention from scratch is practically unnec-
essary, but perhaps not beyond individual capacity. Not coincidentally, cultural
evolutionists typically provide indirect evidence, in the form of ‘lost European
explorer’ narratives (e.g., Boyd et al. 2013): historical anecdotes of expeditions of
well-trained, presumably intelligent people failing in environments where indige-
nous people manage to survive; here, a combination of motivation, available
resources and cognitive capacities was presumably insufficient to re-invent local
subsistence technologies from scratch. A more rigorous search for evidence would
need to establish which technologies lie outside what has recently been called the
‘zone of latent solutions’ (Tennie et al. 2016): behavioral patterns that may be
triggered in sufficiently motivated individuals by social and environmental cues.
Scenarios can be imagined in which this may be tested for various technologies.
One is the ‘‘Island Test’’ proposed by Tomasello (1999): a scenario in which
someone is separated at birth and raised alone on an island, but provided with
sufficient motivation and raw materials to trigger and facilitate re-invention of a
technology in case it is in the ‘zone of latent solutions’. As the difficulties in
realizing this scenario should make clear, currently even our knowledge regarding
this form of technological complexity is easily overstated.
Acknowledgements We thank three anonymous reviewers for their comments on earlier versions of this
manuscript, and the editorial team of Biology & Philosophy for their diligent editorial work. Also, we
thank Lies Mertens and Rianne Schaaf for their help with Figs. 1 and 3. Krist Vaesen acknowledges
support from the Netherlands Organisation for Scientific Research (NWO), Vidi Grant 276-20-021.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, dis-
tribution, and reproduction in any medium, provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were
made.
Complexity and technological evolution: What everybody… 1265
123
References
Baldwin CY, Clark KB (2000) Design rules. Volume 1: the power of modularity. MIT Press, Cambridge
MA
Basalla G (1988) The evolution of technology. Cambridge University Press, Cambridge
Bhattacharya P, Neamtiu I (2011) Assessing programming language impact on development and
maintenance: a study on C and C??. In: ICSE ‘11 proceedings of the 33rd international conference
on software engineering, pp 171–180
Boyd R, Richerson PJ (1996) Why culture is common, but cultural evolution is rare. Proc Brit Acad
88:77–93
Boyd R, Richerson PJ, Henrich J (2013) The cultural evolution of technology: facts and theories. In:
Richerson PJ, Christiansen MH (eds) Cultural evolution: society, technology, language and religion.
MIT Press, Cambridge, pp 119–142
Caldwell CA, Millen AE (2008) Studying cumulative cultural evolution in the laboratory. Philos Trans R
Soc B 363:3529–3539
Caldwell CA, Millen AE (2010) Human cumulative culture in the laboratory: effects of (micro)
population size. Learn Behav 38(3):310–318
Campbell DJ (1984) The effects of goal-contingent payment on the performance of a complex task. Pers
Psychol 37:23–40
Corbett J, Dooner M, Meleka J, Pym C (1991) Design for manufacture: strategies, principles and
techniques. Addison-Wesley Publishers, Boston
Dean LG, Vale GL, Laland KN, Flynn E, Kendal RL (2014) Human cumulative culture: a comparative
perspective. Biol Rev Camb Philos Soc 89(2):284–301
Derex M, Beugin M-P, Godelle B, Raymond M (2013) Experimental evidence for the influence of group
size on cultural complexity. Nature 503:389–391
Enquist M, Ghirlanda S (2007) Evolution of social learning does not explain the origin of human
cumulative culture. J Theor Biol 246:129–135
Enquist M, Ghirlanda S, Jarrick A, Wachtmeister C-A (2008) Why does human culture increase
exponentially? Theor Pop Biol 74:46–55
Enquist M, Ghirlanda S, Eriksson K (2011) Modelling the evolution and diversity of cumulative culture.
Philos Trans R Soc B 366:412–423
Fisher DC (1986) Progress in organismal design. In: Raup DM, Jablonski D (eds) Patterns and processes
in the history of life. Springer, Berlin, pp 99–117
Guthrie RD (1983) Osseous projectile points: biological considerations affecting raw material selection
and design among paleolithic and paleoindian peoples. In: Clutton-Brock J (ed) Animals and
archaeology. Oxford University Press, Oxford, pp 273–294
Henrich J (2004) Demography and cultural evolution: why adaptive cultural processes produced
maladaptive losses in Tasmania. Am Antiq 69:197–214
Henrich J, Heine S, Norenzayan A (2010) The weirdest people in the world? Behav Brain Sci 33:61–83
Holdaway S, Douglas M (2011) A twenty-first century archaeology of stone artifacts. J Archaeol Method
Theory 19:101–131
Jordan P (2014) Technology as human social tradition: cultural transmission among hunter-gatherers
Oakland. University of California Press, California
Karimali E (2005) Lithic technologies and use. In: Blake E, Knapp AB (eds) The archaeology of
mediterranean prehistory. Blackwell, New York, pp 180–214
Kempe M, Lycett SJ, Mesoudi A (2014) From cultural traditions to cumulative culture: parameterizing
the differences between human and nonhuman culture. J Theor Biol 359:29–36
Kolmogorov AN (1974) Complexity of algorithms and objective definition of randomness. Uspekhi Mat
Nauk 29(4):155 (Abstract of a talk at the Moscow Math. Soc. meeting 4/16/1974)
Lake MW, Venti J (2009) Quantitative analysis of macroevolutionary patterning in technological
evolution: bicycle design from 1800 to 2000. In: Shennan S (ed) Pattern and process in cultural
evolution. University of California Press, California, pp 147–162
Lehman H (1947) The exponential increase of man’s cultural output. Soc Forces 25:281–290
Lehmann L, Wakano JY (2013) The handaxe and the microscope: individual and social learning in a
multidimensional model of adaptation. Evolut Hum Behav 34:109–117
Lewis HM, Laland KN (2012) Transmission fidelity is the key to the build-up of cumulative culture.
Philos Trans R Soc B 367:2171–2180
1266 K. Vaesen, W. Houkes
123
Lindenfors P, Ida E, Isaksson S, Magnus E (2015) An empirical study of cultural evolution: the
development of European cooking from medieval to modern times. Cliodynamics 6(2):115–129
March J, Simon H (1958) Organizations. Wiley, New York
McCarthy F (1957) Australia’s aboriginals: their life and culture. Colorgravure, Adelaide
McShea DW (1991) Complexity and evolution: what everybody knows. Biol Philos 6:303–324
McShea DW (1994) Mechanisms of large-scale evolutionary trends. Evolution 48(6):1747–1763
McShea DW, Brandon RN (2010) Biology’s first law. University of Chicago Press, Chicago
Mesoudi A (2011a) Cultural evolution. University of Chicago Press, Chicago
Mesoudi A (2011b) Variable cultural acquisition costs constrain cumulative cultural evolution. PLoS
ONE 6:e18239
Mesoudi A, Laland KN, Boyd R, Buchanan B, Flynn E, McCauley RN, Renn J, Reyes-Garcia V, Shennan
SJ, Stout D, Tennie C (2013) The cultural evolution of technology and science. In: Christiansen MH,
Richerson PJ (eds) Cultural evolution, a strungmann forum report. MIT Press, Cambridge
Mitcham C (1978) Types of technology. Res Philos Technol 1:229–294
Mithen S (1996) The prehistory of the mind: a search for the origins of art, religion and science. Thames
and Hudson Publishers, London
Moore MW, Sutikna T, Jatmiko MM, Brumm A (2009) Continuities in stone flaking technology at Liang
Bua, Flores, Indonesia. J Hum Evolut 57:503–526
Morris I (2013) The measure of civilization: how social development decides the fate of nations.
Princeton University Press, Princeton
Morrison DA (2011) Introduction to phylogenetic networks. RJR Productions, Uppsala
Mott LV (1997) The development of the rudder: a technological tale. Texas A&M University Press,
College Station
Neamtiu I, Xie G, Chen J (2013) Towards a better understanding of software evolution: an empiricalstudy
on open-source software. J Softw Evolut Process 25:193–218
O’Brien MJ, Darwent J, Lyman RL (2001) Cladistics is useful for reconstructing archaeological
phylogenies: palaeoindian points from the southeastern United States. J Arch Sci 28:1115–1136
Oswalt WH (1973) Habitat and technology: the evolution of hunting. Holt, Rinehart, and Winston, New
York
Oswalt WH (1976) An anthropological analysis of food-getting technology. Wiley, New York
Perreault C, Brantingham PJ, Kuhn SL, Wurz S, Gao X (2013) Measuring the complexity of lithic
technology. Curr Anthropol 54(8):S397–S406
Petroski H (1989) The pencil—a history of design and circumstance. Alfred A. Knopf, New York
Petroski H (1992) The evolution of useful things. Vintage books, New York
Powell A, Shennan S, Thomas MG (2009) Late pleistocene demography and the appearance of modern
human behavior. Science 324:1298–1301
Price TD, Brown JA (1985) Aspects of hunter-gatherer complexity. In: Price TD, Brown JA (eds)
Prehistoric hunter-gatherers: the emergence of cultural complexity. Academic, Orlando, pp 3–20
Querbes A, Vaesen K, Houkes W (2014) Complexity and demographic explanations of cumulative
culture. PLoS ONE 9(7):e102543
Richerson P, Boyd R (2005) Not by genes alone: how culture transformed human evolution. University of
Chicago Press, Chicago
Richerson PJ, Boyd R (2008) Response to our critics. Biol Philos 23(2):301–315
Riede F (2009) Tangled trees: modeling material culture evolution as host–associate cospeciation. In:
Shennan S (ed) Pattern and process in cultural evolution. University of California Press, California,
pp 85–98
Ruse M (1988) Molecules to men: evolutionary biology and thoughts of progress. In: Nitecki MH (ed)
Evolutionary progress. University of Chicago Press, Chicago, pp 97–126
Service ER (1962) Profiles in ethnology. Harper and Row, New York
Sessions R (2008) Simple architectures for complex enterprises. Microsoft, Washington
Shea J (2011) Homo sapiens is as homo sapiens was. Curr Anthrop 52(1):1–35
Shott MJ (2003) Chaıne operatoire and reduction sequence. Lithic Technol 28(2):95–105
Simon HA (1962) The architecture of complexity. Proc Am Philos Soc 106:467–482
Soressi M, McPherron SP, Lenoir M, Dogandzic T, Goldberg P, Jacobs Z, Maigrot Y, Martisius NL,
Miller CE, Rendu W, Richards M (2013) Neandertals made the first specialized bone tools in
Europe. Proc Natl Acad Sci 110(35):14186–14190
Stout D (2011) Stone toolmaking and the evolution of human culture and cognition. Philos Trans R Soc B
366:1050–1059
Complexity and technological evolution: What everybody… 1267
123
Strumsky D, Lobo J, van der Leeuw S (2012) Using patent technology codes to study technological
change. Econ Innov New Technol 21(3):267–286
Tehrani JJ, Collard M (2009) On the relationship between interindividual cultural transmission and
population-level cultural diversity: a case study of weaving in Iranian tribal populations. Evolut
Hum Behav 30:286–300
Temkin I, Eldredge N (2007) Phylogenetics and material cultural evolution. Curr Anthropol
48(1):146–154
Tennie C, Call J, Tomasello M (2009) Ratcheting up the ratchet: on the evolution of cumulative culture.
Philos Trans R Soc B 364:2405–2415
Tennie C, Braun DR, Premo LS, McPherron SP (2016) The Island test for cumulative culture in the
paleolithic. In: Haidle MN, Conard NJ, Bolus M (eds) The nature of culture. Springer, Dordrecht,
pp 121–133
Thieme H (1997) Lower palaeolithic hunting spears from Germany. Nature 385:807–810
Tomasello M (1999) The cultural origins of human cognition. Harvard University Press, Cambridge
Tomasello M, Call J (1997) Primate cognition. Oxford University Press, Oxford
Vaesen K (2011) The functional bias of the dual nature of technical artefacts program. Stud Hist Philos
Sci A 42:190–197
Vaesen K (2012) Cumulative cultural evolution and demography. PLoS ONE 7(7):e40989
Vaesen K, Collard M, Cosgrove R, Roebroeks W (2016) Population size does not explain past changes in
cultural complexity. Proc Natl Acad Sci 113(16):E2241–E2247
1268 K. Vaesen, W. Houkes
123